SMART QUESTIONS

How have theft, robbery, and homicide rates in Los Angeles changed from 2020 to the present, and which of these categories shows the highest overall density of crime during this time?
How does the type or frequency of crimes in Los Angeles vary by victim descent, and are there significant geographic patterns (based on latitude and longitude) associated with specific victim groups?
How do crime rates fluctuate during the holiday season, particularly in November and December, and what types of crimes predominantly occur during these months? Additionally, how have these patterns evolved over the years?
How have the top three most common crimes from 2020 to the present been distributed across the top five areas where they are most frequently committed in Los Angeles, and are these trends increasing or decreasing in each area from 2020 to 2024?
Which crimes in Los Angeles exhibited the highest weapon usage, and which ethnicities and genders showed the most significant weapon involvement over the last five years?

crime = read.csv("/Users/amoghramagiri/Documents/mid_term_project/pro/Crime_Data_from_2020_to_Present.csv")
head(crime)

Date Analysis and Cleaning 0.1

nrow(crime)
ncol(crime)

The dataset contains 28 columns and 986500 rows.

DR_NO - Division of Records Number: Official file number.
Date Rptd - MM/DD/YYYY
DATE OCC - MM/DD/YYYY
TIME OCC - In 24 hour military time
AREA - The LAPD has 21 Community Police Stations referred to as Geographic Areas within the department. These Geographic Areas are sequentially numbered from 1-21.
AREA NAME - The 21 Geographic Areas or Patrol Divisions are also given a name designation that references a landmark or the surrounding community that it is responsible for.
Crm Cd - Indicates the crime committed. Crime Code 1 is the primary and most serious one. Crime Code 2, 3, and 4 are respectively less serious offenses. Lower crime class numbers are more serious.
Crm Desc - Indicates the crime description
Vict Age - Age of victim
Vict Sex - F : Female M : Male X : Unknown
Vict Descent - Descent Code: A - Other Asian B - Black C - Chinese D - Cambodian F - Filipino G - Guamanian H - Hispanic/Latin/Mexican I - American Indian/Alaskan Native J - Japanese K - Korean L - Laotian O - Other P - Pacific Islander S - Samoan U - Hawaiian V - Vietnamese W - White X - Unknown Z - Asian Indian
Weapon Desc - Defines the Weapon Used Code provided.
Location - Street address of crime incident rounded to the nearest hundred block to maintain anonymity.
LAT - Latitude
LON - Longtitude

na_count <- colSums(is.na(crime))
print("\nNA Count per Column:")

## [1] "\nNA Count per Column:"

print(na_count)

##          DR_NO      Date.Rptd       DATE.OCC       TIME.OCC           AREA 
##              0              0              0              0              0 
##      AREA.NAME    Rpt.Dist.No       Part.1.2         Crm.Cd    Crm.Cd.Desc 
##              0              0              0              0              0 
##        Mocodes       Vict.Age       Vict.Sex   Vict.Descent      Premis.Cd 
##              0              0              0              0             14 
##    Premis.Desc Weapon.Used.Cd    Weapon.Desc         Status    Status.Desc 
##              0         660132              0              0              0 
##       Crm.Cd.1       Crm.Cd.2       Crm.Cd.3       Crm.Cd.4       LOCATION 
##             11         917588         984190         986436              0 
##   Cross.Street            LAT            LON 
##              0              0              0

new_crime <- crime[, colSums(is.na(crime)) == 0]
head(new_crime)

##       DR_NO              Date.Rptd               DATE.OCC TIME.OCC AREA
## 1 190326475 03/01/2020 12:00:00 AM 03/01/2020 12:00:00 AM     2130    7
## 2 200106753 02/09/2020 12:00:00 AM 02/08/2020 12:00:00 AM     1800    1
## 3 200320258 11/11/2020 12:00:00 AM 11/04/2020 12:00:00 AM     1700    3
## 4 200907217 05/10/2023 12:00:00 AM 03/10/2020 12:00:00 AM     2037    9
## 5 220614831 08/18/2022 12:00:00 AM 08/17/2020 12:00:00 AM     1200    6
## 6 231808869 04/04/2023 12:00:00 AM 12/01/2020 12:00:00 AM     2300   18
##   AREA.NAME Rpt.Dist.No Part.1.2 Crm.Cd
## 1  Wilshire         784        1    510
## 2   Central         182        1    330
## 3 Southwest         356        1    480
## 4  Van Nuys         964        1    343
## 5 Hollywood         666        2    354
## 6 Southeast        1826        2    354
##                                Crm.Cd.Desc             Mocodes Vict.Age
## 1                         VEHICLE - STOLEN                            0
## 2                    BURGLARY FROM VEHICLE      1822 1402 0344       47
## 3                            BIKE - STOLEN           0344 1251       19
## 4 SHOPLIFTING-GRAND THEFT ($950.01 & OVER)           0325 1501       19
## 5                        THEFT OF IDENTITY 1822 1501 0930 2004       28
## 6                        THEFT OF IDENTITY 1822 0100 0930 0929       41
##   Vict.Sex Vict.Descent                                  Premis.Desc
## 1        M            O                                       STREET
## 2        M            O            BUS STOP/LAYOVER (ALSO QUERY 124)
## 3        X            X MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)
## 4        M            O                               CLOTHING STORE
## 5        M            H                                     SIDEWALK
## 6        M            H                       SINGLE FAMILY DWELLING
##   Weapon.Desc Status  Status.Desc                                 LOCATION
## 1                 AA Adult Arrest  1900 S  LONGWOOD                     AV
## 2                 IC  Invest Cont  1000 S  FLOWER                       ST
## 3                 IC  Invest Cont  1400 W  37TH                         ST
## 4                 IC  Invest Cont 14000    RIVERSIDE                    DR
## 5                 IC  Invest Cont                        1900    TRANSIENT
## 6                 IC  Invest Cont  9900    COMPTON                      AV
##   Cross.Street  LAT  LON
## 1              34.0 -118
## 2              34.0 -118
## 3              34.0 -118
## 4              34.2 -118
## 5              34.1 -118
## 6              33.9 -118

nrow(new_crime)

## [1] 986500

ncol(new_crime)

## [1] 22

Removing Unwanted Columns 0.2

# Feature Selection based on Smart Questions

cols_to_remove <- c(
   "Mocodes", "Rpt.Dist.No", "Part.1.2", 
  "Premis_cd","Premis.Desc", "Status", "Status.Desc","Cross.Street"
)

# Drop the specified columns
crime_data <- new_crime[, !(names(new_crime) %in% cols_to_remove)]


print("Data after removing unnecessary columns:")

## [1] "Data after removing unnecessary columns:"

print(names(crime_data))

##  [1] "DR_NO"        "Date.Rptd"    "DATE.OCC"     "TIME.OCC"     "AREA"        
##  [6] "AREA.NAME"    "Crm.Cd"       "Crm.Cd.Desc"  "Vict.Age"     "Vict.Sex"    
## [11] "Vict.Descent" "Weapon.Desc"  "LOCATION"     "LAT"          "LON"

Question 1

How have the top three most common crimes from 2020 to the present been distributed across the top five areas where they are most frequently committed in Los Angeles, and are these trends increasing or decreasing in each area from 2020 to 2024?

1. First, let’s identify the top 3 crimes and top 5 areas

library(dplyr)
library(ggplot2)
library(lubridate)

# Ensure date is in correct format
crime_data$DATE.OCC <- as.Date(crime_data$DATE.OCC, format = "%m/%d/%Y")


# Identify top 3 crimes overall from 2020 to present
top_crimes <- crime_data %>%
  filter(year(DATE.OCC) >= 2020 & year(DATE.OCC) <= 2023 ) %>%
  group_by(Crm.Cd, Crm.Cd.Desc) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  head(3)
crime_summary <- crime_data %>%
  filter(year(DATE.OCC) >= 2020) %>%
  group_by(AREA.NAME) %>%
  summarise(
    total_crime = n(),
    top3_crime = sum(Crm.Cd %in% top_crimes$Crm.Cd)
  ) %>%
  mutate(top3_pct = top3_crime / total_crime * 100)
top_areas <- crime_summary %>%
  arrange(desc(top3_crime)) %>%
  head(5)

# Filter data for top 3 crimes from 2020 onwards
crime_filtered <- crime_data %>%
  filter(year(DATE.OCC) >= 2020, Crm.Cd %in% top_crimes$Crm.Cd)


# Create separate plots with angled x-axis labels
p1 <- ggplot(
  subset(crime_filtered, Crm.Cd.Desc == "BATTERY - SIMPLE ASSAULT"),
  aes(x = AREA.NAME)
) +
  geom_bar(fill = "#4e79a7") +
  theme_minimal() +
  labs(
    title = "BATTERY - SIMPLE ASSAULT Distribution by Area",
    x = "Area Name",
    y = "Number of Incidents"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),  # Angled labels
    panel.grid.minor = element_blank(),
    plot.margin = margin(20, 20, 40, 20)  # Increased bottom margin for labels
  )

p2 <- ggplot(
  subset(crime_filtered, Crm.Cd.Desc == "THEFT OF IDENTITY"),
  aes(x = AREA.NAME)
) +
  geom_bar(fill = "#3d8f7d") +
  theme_minimal() +
  labs(
    title = "THEFT OF IDENTITY Distribution by Area",
    x = "Area Name",
    y = "Number of Incidents"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),  # Angled labels
    panel.grid.minor = element_blank(),
    plot.margin = margin(20, 20, 40, 20)  # Increased bottom margin for labels
  )

p3 <- ggplot(
  subset(crime_filtered, Crm.Cd.Desc == "VEHICLE - STOLEN"),
  aes(x = AREA.NAME)
) +
  geom_bar(fill = "#59a14f") +
  theme_minimal() +
  labs(
    title = "VEHICLE - STOLEN Distribution by Area",
    x = "Area Name",
    y = "Number of Incidents"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),  # Angled labels
    panel.grid.minor = element_blank(),
    plot.margin = margin(20, 20, 40, 20)  # Increased bottom margin for labels
  )

# Display plots one after another
print(p1)

print(p2)

print(p3)

Top 3 Crimes:

BATTERY - SIMPLE ASSAULT THEFT OF IDENTITY VEHICLE - STOLEN

2. Visualization Crime Trends by type and area

# 1. Create top_crimes (2020-2023)
top_crimes <- crime_data %>%
  filter(year(DATE.OCC) >= 2020 & year(DATE.OCC) <= 2023) %>%
  group_by(Crm.Cd, Crm.Cd.Desc) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  head(3)

# 2. Create top_areas (2020-2023)
top_areas <- crime_data %>%
  filter(year(DATE.OCC) >= 2020 & year(DATE.OCC) <= 2023) %>%
  group_by(AREA.NAME) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  head(5)

# 3. Create crime_counts with verified column names
crime_counts <- crime_data %>%
  filter(year(DATE.OCC) >= 2020 & year(DATE.OCC) <= 2023,
         AREA.NAME %in% top_areas$AREA.NAME,
         Crm.Cd %in% top_crimes$Crm.Cd) %>%
  group_by(AREA.NAME, Year = year(DATE.OCC), Crm.Cd.Desc) %>%
  summarise(crime_count = n(), .groups = 'drop')

# 4. Trend analysis
trend_analysis <- crime_counts %>%
  group_by(AREA.NAME, Crm.Cd.Desc) %>%
  summarise(
    p_value = summary(lm(crime_count ~ Year))$coefficients[2,4],
    r_squared = summary(lm(crime_count ~ Year))$r.squared,
    .groups = 'drop'
  )

# 5. Visualization
p1 <- ggplot(crime_counts,
             aes(x = Year, y = crime_count, color = AREA.NAME)) +
  geom_line() +
  geom_point() +
  facet_wrap(~Crm.Cd.Desc, scales = "free_y") +
  theme_minimal() +
  labs(title = "Crime Trends by Type and Area (2020-2023)",
       x = "Year",
       y = "Crime Count")

# Print results
print("\nTrend Analysis Results:")

## [1] "\nTrend Analysis Results:"

print(trend_analysis)

## # A tibble: 15 × 4
##    AREA.NAME   Crm.Cd.Desc              p_value r_squared
##    <chr>       <chr>                      <dbl>     <dbl>
##  1 77th Street BATTERY - SIMPLE ASSAULT 0.0991   0.812   
##  2 77th Street THEFT OF IDENTITY        0.460    0.291   
##  3 77th Street VEHICLE - STOLEN         0.0374   0.927   
##  4 Central     BATTERY - SIMPLE ASSAULT 0.0624   0.879   
##  5 Central     THEFT OF IDENTITY        0.399    0.361   
##  6 Central     VEHICLE - STOLEN         0.0333   0.935   
##  7 Hollywood   BATTERY - SIMPLE ASSAULT 0.165    0.698   
##  8 Hollywood   THEFT OF IDENTITY        0.697    0.0916  
##  9 Hollywood   VEHICLE - STOLEN         0.597    0.163   
## 10 Pacific     BATTERY - SIMPLE ASSAULT 0.260    0.548   
## 11 Pacific     THEFT OF IDENTITY        0.221    0.608   
## 12 Pacific     VEHICLE - STOLEN         0.171    0.687   
## 13 Southwest   BATTERY - SIMPLE ASSAULT 0.970    0.000872
## 14 Southwest   THEFT OF IDENTITY        0.541    0.211   
## 15 Southwest   VEHICLE - STOLEN         0.00149  0.997

print(p1)

1. Strong Evidence of Trends (p < 0.05): # - Vehicle theft shows significant patterns in three areas (Southwest p=0.00149, Central p=0.03326, 77th Street p=0.03744) # - These trends are highly reliable with R² > 0.92, suggesting strong predictability

Notable but Not Significant: # - Battery cases show strong patterns (R² > 0.81) but fall just short of significance (p ≈ 0.06-0.10) # - Pacific area shows consistent moderate patterns (R² > 0.54) across all crime types
Areas Needing Further Investigation: # - Theft of Identity shows weak patterns across most areas (R² < 0.36) # - Southwest Battery shows no clear trend (R² = 0.001, p = 0.97)

3. Visulatization - Percent Change in Top 3 Crimes from 2020 Benchmark - Top 5 Areas

library(dplyr)
library(ggplot2)
library(lubridate)

# Date formatting
crime_data$DATE.OCC <- as.Date(crime_data$DATE.OCC, format = "%m/%d/%Y")

# top 3 crimes in top 5 areas
crime_counts <- crime_data %>%

  filter(year(DATE.OCC) >= 2020 & year(DATE.OCC) <= 2023,
         AREA.NAME %in% top_areas$AREA.NAME,
         Crm.Cd %in% top_crimes$Crm.Cd) %>%
  group_by(AREA.NAME, Year = year(DATE.OCC), Crm.Cd.Desc) %>%
  summarise(crime_count = n()) %>%
  ungroup()

# Percent change from 2020 benchmark
crime_change <- crime_counts %>%
  group_by(AREA.NAME, Crm.Cd.Desc) %>%
  mutate(
    benchmark_2020 = crime_count[Year == 2020],
    percent_change = (crime_count - benchmark_2020) / benchmark_2020 * 100
  ) %>%
  ungroup()

# line graph showing percent change from 2020 benchmark for each area
p <- ggplot(crime_change, aes(x = Year, y = percent_change, color = Crm.Cd.Desc, group = Crm.Cd.Desc)) +
  geom_line() +
  geom_point() +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  facet_wrap(~ AREA.NAME, ncol = 2) +
  theme_minimal() +
  labs(title = "Percent Change in Top 3 Crimes from 2020 Benchmark - Top 5 Areas",
       x = "Year",
       y = "Percent Change from 2020",
       color = "Crime Type") +
  theme(legend.position = "bottom",
        plot.title = element_text(hjust = 0.5, size = 10)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1))

print(p)

print(crime_change)

## # A tibble: 60 × 6
##    AREA.NAME    Year Crm.Cd.Desc       crime_count benchmark_2020 percent_change
##    <chr>       <dbl> <chr>                   <int>          <int>          <dbl>
##  1 77th Street  2020 BATTERY - SIMPLE…         985            985           0   
##  2 77th Street  2020 THEFT OF IDENTITY         446            446           0   
##  3 77th Street  2020 VEHICLE - STOLEN         1589           1589           0   
##  4 77th Street  2021 BATTERY - SIMPLE…        1012            985           2.74
##  5 77th Street  2021 THEFT OF IDENTITY         542            446          21.5 
##  6 77th Street  2021 VEHICLE - STOLEN         1774           1589          11.6 
##  7 77th Street  2022 BATTERY - SIMPLE…        1050            985           6.60
##  8 77th Street  2022 THEFT OF IDENTITY        2039            446         357.  
##  9 77th Street  2022 VEHICLE - STOLEN         1804           1589          13.5 
## 10 77th Street  2023 BATTERY - SIMPLE…        1236            985          25.5 
## # ℹ 50 more rows

YEAR-OVER-YEAR CRIME TREND ANALYSIS (2020-2023)

Battery - Simple Assault: # - Central shows highest spikes in 2021-2022 (~25%) # - Hollywood shows initial increase then decline # - Pacific maintains moderate, stable increases # - 77th Street shows fluctuating pattern
Theft of Identity: # - 77th Street shows dramatic spike in 2022 (~250%) # - Southwest shows significant increase in 2022 (~200%) # - All areas show sharp decline in 2023 # - Central shows more moderate increases
Vehicle - Stolen: # - Central shows consistent high increases (40% in 2021-2022) # - Hollywood shows decline in 2023 # - Pacific and Southwest show moderate increases # - More stable patterns than other crime types
Overall Patterns: # - 2022 was peak year for most crimes # - 2023 shows general declining trend # - Central area shows highest volatility # - Different crime types show distinct patterns by area

4. Year-over-Year Percentage Change in Crime Counts

# Load necessary libraries
library(dplyr)
library(ggplot2)

# 1. Summary Statistics
summary_stats <- crime_counts %>%
  group_by(AREA.NAME, Crm.Cd.Desc) %>%
  summarise(
    mean_count = mean(crime_count, na.rm = TRUE),
    sd_count = sd(crime_count, na.rm = TRUE),
    min_count = min(crime_count, na.rm = TRUE),
    max_count = max(crime_count, na.rm = TRUE),
    .groups = 'drop'  # This is important to avoid grouped data frame issues
  )

# 2. ANOVA test (corrected)
anova_test <- aov(crime_count ~ AREA.NAME + Crm.Cd.Desc, data = crime_counts)
anova_summary <- summary(anova_test)

# 3. Linear Regression for Trend Analysis
trend_analysis <- crime_counts %>%
  group_by(AREA.NAME, Crm.Cd.Desc) %>%
  do(model = lm(crime_count ~ Year, data = .))

# 4. Calculate year-over-year changes
yoy_changes <- crime_counts %>%
  group_by(AREA.NAME, Crm.Cd.Desc) %>%
  arrange(Year) %>%
  mutate(
    prev_year_count = lag(crime_count),
    pct_change = (crime_count - prev_year_count) / prev_year_count * 100
  ) %>%
  ungroup()  # Ungroup to avoid issues in plotting

# Create the visualization
ggplot(yoy_changes,
       aes(x = as.factor(Year),
           y = pct_change,
           fill = AREA.NAME)) + 
  geom_bar(stat = "identity", position = "dodge", width = 0.7) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
  facet_wrap(~Crm.Cd.Desc, scales = "free_y") +
  theme_minimal() +
  scale_fill_brewer(palette = "Set2") +
  labs(title = "Year-over-Year Percentage Change in Crime Counts",
       subtitle = "By Area and Crime Type (2020-2023)",
       x = "Year",
       y = "Percentage Change (%)",
       fill = "Area") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12),
    plot.subtitle = element_text(hjust = 0.5, size = 10),
    axis.text = element_text(size = 10),
    legend.position = "bottom",
    strip.text = element_text(size = 10, face = "bold")
  )

# Print results
print("Summary Statistics:")

## [1] "Summary Statistics:"

print(summary_stats)

## # A tibble: 15 × 6
##    AREA.NAME   Crm.Cd.Desc              mean_count sd_count min_count max_count
##    <chr>       <chr>                         <dbl>    <dbl>     <int>     <int>
##  1 77th Street BATTERY - SIMPLE ASSAULT      1071.    113.        985      1236
##  2 77th Street THEFT OF IDENTITY              998.    730.        446      2039
##  3 77th Street VEHICLE - STOLEN              1805     191.       1589      2053
##  4 Central     BATTERY - SIMPLE ASSAULT      1608.    254.       1340      1829
##  5 Central     THEFT OF IDENTITY              443.    216.        248       744
##  6 Central     VEHICLE - STOLEN               970.    310.        594      1247
##  7 Hollywood   BATTERY - SIMPLE ASSAULT      1048     129.        879      1181
##  8 Hollywood   THEFT OF IDENTITY              590.    113.        478       735
##  9 Hollywood   VEHICLE - STOLEN               827      90.9       709       901
## 10 Pacific     BATTERY - SIMPLE ASSAULT       767      40.5       734       826
## 11 Pacific     THEFT OF IDENTITY              629.    126.        464       766
## 12 Pacific     VEHICLE - STOLEN              1345     174.       1086      1453
## 13 Southwest   BATTERY - SIMPLE ASSAULT      1034.     43.7       972      1074
## 14 Southwest   THEFT OF IDENTITY              862.    567.        473      1692
## 15 Southwest   VEHICLE - STOLEN              1334.    220.       1068      1586

print("\nANOVA Results:")

## [1] "\nANOVA Results:"

print(anova_summary)

##             Df  Sum Sq Mean Sq F value  Pr(>F)    
## AREA.NAME    4 1530409  382602    2.97   0.028 *  
## Crm.Cd.Desc  2 3252930 1626465   12.62 3.3e-05 ***
## Residuals   53 6833036  128925                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Year-over-Year Trends by Crime Type: # Battery - Simple Assault: # - Peak increases in 2021-2022 # - Central area showed highest variability # - Hollywood shows declining trend in 2023

# Theft of Identity: # - Dramatic spike in 2022 (>200% in some areas) # - Consistent decline in 2023 across all areas # - 77th Street shows most stable pattern

# Vehicle - Stolen: # - Central area shows persistent increase # - Most areas peak in 2021-2022 # - Varying patterns of decline in 2023

Key Findings for Policy: # - Crime type is stronger predictor than location # - 2022 was peak year for most crime types # - Different crimes show distinct geographic patterns

Question 2

Which neighborhoods in Los Angeles have experienced the most significant increases in crime rates from 2020 to 2023 with particular attention to demographic factors (race and sex of victims), and what insights can be drawn from Area3’s significant increase in crimes?

1. Calculating Year-Over-Year Percentage Change:-

library(tidyr)  # Load tidyr for pivot_wider function
library(knitr)  # Load knitr for kable function

# Filter to include only years up to 2023
crime_data_filtered <- crime_data %>%
  filter(year(DATE.OCC) < 2024)

# Calculate year-over-year percentage changes
crime_summary <- crime_data_filtered %>%
  mutate(Year = year(DATE.OCC)) %>%
  filter(Year %in% c(2020, 2023)) %>%  # Only for years 2020 and 2023
  group_by(AREA.NAME, Year) %>%
  summarise(crime_count = n(), .groups = "drop") %>%
  pivot_wider(names_from = Year, values_from = crime_count, values_fill = list(crime_count = 0)) %>%
  # Calculate percent change from 2020 to 2023
  mutate(percent_change_2020_2023 = ((`2023` - `2020`) / `2020`) * 100)

# Display the results
kable(crime_summary, format = "markdown", digits = 2)

AREA.NAME	2020	2023	percent_change_2020_2023
77th Street	13343	13944	4.50
Central	11600	16957	46.18
Devonshire	7982	9762	22.30
Foothill	7106	7155	0.69
Harbor	8874	9125	2.83
Hollenbeck	7805	8401	7.64
Hollywood	10171	11447	12.55
Mission	8480	8973	5.81
N Hollywood	10167	11548	13.58
Newton	9994	11926	19.33
Northeast	8452	9707	14.85
Olympic	9639	11739	21.79
Pacific	11575	13772	18.98
Rampart	9025	11527	27.72
Southeast	10847	11288	4.07
Southwest	11178	13109	17.28
Topanga	8106	9636	18.87
Van Nuys	8763	9936	13.39
West LA	9309	10578	13.63
West Valley	8091	9930	22.73
Wilshire	9291	11707	26.00

2. Top 3 Crimes in Areas with Highest Increases :-

# Ensure we have the correct top areas based on overall increase from 2020 to 2023
top_areas <- crime_summary %>%
  arrange(desc(percent_change_2020_2023)) %>%
  head(5) %>%
  pull(AREA.NAME)

# Calculate crime counts and percentage change by area and crime type
crime_analysis <- crime_data %>%
  filter(year(DATE.OCC) %in% c(2020, 2023), AREA.NAME %in% top_areas) %>%
  mutate(Year = year(DATE.OCC)) %>%
  group_by(AREA.NAME, Crm.Cd.Desc, Year) %>%
  summarise(count = n(), .groups = "drop") %>%
  pivot_wider(names_from = Year, values_from = count) %>%
  # Only fill missing years with 0 where genuinely needed after checking
  mutate(
    count_2020 = replace_na(`2020`, 0),
    count_2023 = replace_na(`2023`, 0),
    percent_change = ifelse(count_2020 > 0, ((count_2023 - count_2020) / count_2020) * 100, NA),
    absolute_change = count_2023 - count_2020
  ) %>%
  filter(!is.na(percent_change)) %>%
  arrange(AREA.NAME, desc(percent_change))

# Get top 3 most increased crimes for each area
top_crimes_by_area <- crime_analysis %>%
  group_by(AREA.NAME) %>%
  filter(percent_change > 0) %>%  # Only include increases
  slice_max(percent_change, n = 3) %>%  # Get top 3 crimes by percent change
  arrange(AREA.NAME, desc(percent_change))

# Display the results
print(top_crimes_by_area)

## # A tibble: 15 × 8
## # Groups:   AREA.NAME [5]
##    AREA.NAME   Crm.Cd.Desc    `2020` `2023` count_2020 count_2023 percent_change
##    <chr>       <chr>           <int>  <int>      <int>      <int>          <dbl>
##  1 Central     VEHICLE, STOL…      1     56          1         56          5500 
##  2 Central     PICKPOCKET         15    280         15        280          1767.
##  3 Central     SHOPLIFTING-G…     15     71         15         71           373.
##  4 Devonshire  VEHICLE, STOL…      1     28          1         28          2700 
##  5 Devonshire  PICKPOCKET          1     12          1         12          1100 
##  6 Devonshire  SODOMY/SEXUAL…      1      9          1          9           800 
##  7 Rampart     PICKPOCKET          2     50          2         50          2400 
##  8 Rampart     VEHICLE, STOL…      2     41          2         41          1950 
##  9 Rampart     SHOPLIFTING-G…      6    120          6        120          1900 
## 10 West Valley BOMB SCARE          1     11          1         11          1000 
## 11 West Valley STALKING            1      4          1          4           300 
## 12 West Valley BATTERY ON A …      2      7          2          7           250 
## 13 Wilshire    VEHICLE, STOL…      1     34          1         34          3300 
## 14 Wilshire    PICKPOCKET          5     77          5         77          1440 
## 15 Wilshire    SHOPLIFTING -…    216   1381        216       1381           539.
## # ℹ 1 more variable: absolute_change <int>

Based on the year-over-year percentage change analysis from 2020 to 2023:

Central showed the most significant increase in crime rate, with a 46.18% rise, followed by Wilshire at 26% and Rampart at 27.72%. These areas have seen substantial crime growth over the past few years, indicating potential hotspots for criminal activity.
Some neighborhoods, such as 77th Street and Southeast, had minimal increases, suggesting more stable crime rates or possible effectiveness in crime control efforts.
The variety in percentage changes across neighborhoods highlights that crime rate increases are not uniformly distributed in Los Angeles, with certain areas experiencing much higher growth than others, signaling where law enforcement or community initiatives might prioritize resources.

3. Crime Change Analysis by Sex and Race :-

library(dplyr)
library(ggplot2)
library(lubridate)

# Crime changes by Area, Crime Type, Sex, and Race
crime_analysis <- crime_data %>%
  filter(year(DATE.OCC) %in% c(2020, 2023)) %>%
  group_by(AREA.NAME, Crm.Cd.Desc, Vict.Sex, Vict.Descent, Year = year(DATE.OCC)) %>%
  summarise(count = n(), .groups = "drop") %>%
  pivot_wider(names_from = Year, 
              values_from = count, 
              names_prefix = "count_") %>%
  mutate(
    percent_change = ((count_2023 - count_2020) / count_2020) * 100,
    absolute_change = count_2023 - count_2020
  ) %>%
  filter(!is.na(percent_change))


# 1. Crime Change by Area and Sex
filtered_crime_analysis <- crime_analysis %>%
  filter(Vict.Sex %in% c("M", "F", "X"))
p1 <- ggplot(filtered_crime_analysis, 
       aes(x = AREA.NAME, y = percent_change, fill = Vict.Sex)) +
  geom_bar(stat = "identity", position = "dodge") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Crime Change by Area and Victim Sex (2020-2023)",
       x = "Area",
       y = "Percent Change",
       fill = "Victim Sex")

# 2. Simplified Crime Change by Area and Victim Descent
# Filtering top 5 areas and focusing on significant changes

top_areas <- crime_summary %>%
  arrange(desc(percent_change_2020_2023)) %>%
  head(5) %>%
  pull(AREA.NAME)

# Filter the data for top areas and descent groups with significant changes
simplified_crime_analysis <- crime_analysis %>%
  filter(AREA.NAME %in% top_areas) %>%
  group_by(AREA.NAME, Vict.Descent) %>%
  summarise(avg_percent_change = mean(percent_change, na.rm = TRUE), .groups = 'drop') %>%
  filter(avg_percent_change > 100)  # Threshold to highlight significant changes

# Create the simplified plot
crime_analysis_filtered <- crime_analysis %>%
  filter(Vict.Descent != "X") %>%  # Remove unknown values
  group_by(AREA.NAME, Vict.Descent) %>%
  summarise(percent_change = mean(percent_change, na.rm = TRUE))

# Create the plot with filtered data
p2 <- ggplot(simplified_crime_analysis, 
       aes(x = AREA.NAME, y = avg_percent_change, fill = Vict.Descent)) +
  geom_bar(stat = "identity", position = "dodge") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Significant Crime Change by Area and Victim Descent (Top 5 Areas, 2020-2023)",
       x = "Area",
       y = "Average Percent Change",
       fill = "Victim Descent") +
  scale_fill_brewer(palette = "Set3")




print(p1)

print(p2)

# Summary table
summary_table <- crime_analysis %>%
  group_by(AREA.NAME, Vict.Sex, Vict.Descent) %>%
  summarise(
    avg_percent_change = mean(percent_change, na.rm = TRUE),
    total_crimes_2020 = sum(count_2020, na.rm = TRUE),
    total_crimes_2023 = sum(count_2023, na.rm = TRUE)
  ) %>%
  arrange(desc(avg_percent_change))

print(summary_table)

## # A tibble: 498 × 6
## # Groups:   AREA.NAME, Vict.Sex [85]
##    AREA.NAME   Vict.Sex Vict.Descent avg_percent_change total_crimes_2020
##    <chr>       <chr>    <chr>                     <dbl>             <int>
##  1 Wilshire    "X"      "X"                       2159.               114
##  2 N Hollywood "X"      "X"                       1415.                29
##  3 Foothill    "X"      "X"                       1128.                11
##  4 Devonshire  ""       ""                         747.               946
##  5 Harbor      ""       ""                         737.              1276
##  6 Olympic     "F"      "C"                        652                  9
##  7 Southeast   "F"      "I"                        600                  1
##  8 Central     "M"      "I"                        550                  2
##  9 Topanga     ""       ""                         541.               864
## 10 Devonshire  "F"      "C"                        500                  1
## # ℹ 488 more rows
## # ℹ 1 more variable: total_crimes_2023 <int>

2020 to 2023, we observe the following:

A = “Other Asian”,B = “Black”, C = “Chinese”, D = “Cambodian”, F = “Filipino”, G = “Guamanian”, H = “Hispanic/Latin/Mexican”, I = “American Indian/Alaskan Native”, J = “Japanese”, K = “Korean”, L = “Laotian”, O = “Other”, P = “Pacific Islander”, S = “Samoan”, U = “Hawaiian”, V = “Vietnamese”, W = “White”, X = “Unknown”, Z = “Asian Indian”

High Growth in Wilshire and North Hollywood: Neighborhoods like Wilshire and North Hollywood show notable increases in crime rates across several demographics, especially for victims whose descent or sex is marked as unknown (“X”).
Descent-Based Trends: Certain descent groups, such as “C” (likely representing specific ethnic codes in the dataset), appear prominently in the Wilshire area, indicating targeted impacts or reporting discrepancies.
Sex-Based Analysis: The data shows varied increases across different sexes in certain areas, with Wilshire and West Valley seeing substantial growth in incidents for male and unknown (X) demographics.
Need for Targeted Focus: Areas with significant increases, especially Wilshire, show the importance of focused interventions in particular demographics to understand and address underlying causes.

Question 3

How do crime rates fluctuate during the holiday season, particularly in November and December, and what types of crimes predominantly occur during these months? Additionally, how have these patterns evolved over the years?

head(crime$Date.Rptd)

## [1] "03/01/2020 12:00:00 AM" "02/09/2020 12:00:00 AM" "11/11/2020 12:00:00 AM"
## [4] "05/10/2023 12:00:00 AM" "08/18/2022 12:00:00 AM" "04/04/2023 12:00:00 AM"

library(dplyr)
library(ggplot2)
library(lubridate)
library(plotly)

# Display the structure to understand the format of the Date column
str(crime)

## 'data.frame':    986500 obs. of  28 variables:
##  $ DR_NO         : int  190326475 200106753 200320258 200907217 220614831 231808869 230110144 220314085 231309864 211904005 ...
##  $ Date.Rptd     : chr  "03/01/2020 12:00:00 AM" "02/09/2020 12:00:00 AM" "11/11/2020 12:00:00 AM" "05/10/2023 12:00:00 AM" ...
##  $ DATE.OCC      : chr  "03/01/2020 12:00:00 AM" "02/08/2020 12:00:00 AM" "11/04/2020 12:00:00 AM" "03/10/2020 12:00:00 AM" ...
##  $ TIME.OCC      : int  2130 1800 1700 2037 1200 2300 900 1110 1400 1220 ...
##  $ AREA          : int  7 1 3 9 6 18 1 3 13 19 ...
##  $ AREA.NAME     : chr  "Wilshire" "Central" "Southwest" "Van Nuys" ...
##  $ Rpt.Dist.No   : int  784 182 356 964 666 1826 182 303 1375 1974 ...
##  $ Part.1.2      : int  1 1 1 1 2 2 2 2 2 2 ...
##  $ Crm.Cd        : int  510 330 480 343 354 354 354 354 354 624 ...
##  $ Crm.Cd.Desc   : chr  "VEHICLE - STOLEN" "BURGLARY FROM VEHICLE" "BIKE - STOLEN" "SHOPLIFTING-GRAND THEFT ($950.01 & OVER)" ...
##  $ Mocodes       : chr  "" "1822 1402 0344" "0344 1251" "0325 1501" ...
##  $ Vict.Age      : int  0 47 19 19 28 41 25 27 24 26 ...
##  $ Vict.Sex      : chr  "M" "M" "X" "M" ...
##  $ Vict.Descent  : chr  "O" "O" "X" "O" ...
##  $ Premis.Cd     : int  101 128 502 405 102 501 502 248 750 502 ...
##  $ Premis.Desc   : chr  "STREET" "BUS STOP/LAYOVER (ALSO QUERY 124)" "MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)" "CLOTHING STORE" ...
##  $ Weapon.Used.Cd: int  NA NA NA NA NA NA NA NA NA 400 ...
##  $ Weapon.Desc   : chr  "" "" "" "" ...
##  $ Status        : chr  "AA" "IC" "IC" "IC" ...
##  $ Status.Desc   : chr  "Adult Arrest" "Invest Cont" "Invest Cont" "Invest Cont" ...
##  $ Crm.Cd.1      : int  510 330 480 343 354 354 354 354 354 624 ...
##  $ Crm.Cd.2      : int  998 998 NA NA NA NA NA NA NA NA ...
##  $ Crm.Cd.3      : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ Crm.Cd.4      : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ LOCATION      : chr  "1900 S  LONGWOOD                     AV" "1000 S  FLOWER                       ST" "1400 W  37TH                         ST" "14000    RIVERSIDE                    DR" ...
##  $ Cross.Street  : chr  "" "" "" "" ...
##  $ LAT           : num  34 34 34 34.2 34.1 ...
##  $ LON           : num  -118 -118 -118 -118 -118 ...

# Print the first few entries of the Date.Rptd column to check its format
print(head(crime$Date.Rptd))

## [1] "03/01/2020 12:00:00 AM" "02/09/2020 12:00:00 AM" "11/11/2020 12:00:00 AM"
## [4] "05/10/2023 12:00:00 AM" "08/18/2022 12:00:00 AM" "04/04/2023 12:00:00 AM"

# Assuming the date is in a column named 'Date.Rptd' and format is 'mm/dd/yyyy'
crime$Date.Rptd <- as.Date(crime$Date.Rptd, format="%m/%d/%Y")

# Check if there were any conversion errors
sum(is.na(crime$Date.Rptd))

## [1] 0

# Extract Year, Month, and Day from the Date.Rptd
crime$Year <- year(crime$Date.Rptd)
crime$Month <- month(crime$Date.Rptd)
crime$Day <- day(crime$Date.Rptd)

# Summarize total crimes by month and year
monthly_crime_summary <- crime %>%
  group_by(Year, Month) %>%
  summarise(Total_Crimes = n(), .groups = 'drop')

# Plot total crimes by month and year
mcs <- ggplot(monthly_crime_summary, aes(x = Month, y = Total_Crimes, group = Year, color = as.factor(Year))) +
  geom_line() +
  labs(title = "Monthly Crime Trends by Year", x = "Month", y = "Total Crimes") +
  scale_x_continuous(breaks = 1:12, labels = month.abb)

ggplotly(mcs)

# Analyzing crime types during November and December across all years
holiday_crimes <- crime %>%
  filter(Month %in% c(11, 12)) %>%
  group_by(Year, Crm.Cd.Desc) %>%
  summarise(Total_Crimes = n(), .groups = 'drop') %>%
  arrange(Year, desc(Total_Crimes))

# Print the top crime types during holidays
print(holiday_crimes)

## # A tibble: 455 × 3
##     Year Crm.Cd.Desc                                             Total_Crimes
##    <dbl> <chr>                                                          <int>
##  1  2020 VEHICLE - STOLEN                                                3522
##  2  2020 BATTERY - SIMPLE ASSAULT                                        2433
##  3  2020 BURGLARY                                                        2071
##  4  2020 BURGLARY FROM VEHICLE                                           1960
##  5  2020 VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)         1957
##  6  2020 ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT                  1933
##  7  2020 THEFT PLAIN - PETTY ($950 & UNDER)                              1741
##  8  2020 INTIMATE PARTNER - SIMPLE ASSAULT                               1621
##  9  2020 THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)                 1411
## 10  2020 THEFT OF IDENTITY                                               1260
## # ℹ 445 more rows

# Plotting top crime types during holiday months
top_crime_types <- holiday_crimes %>%
  group_by(Crm.Cd.Desc) %>%
  summarise(Total_Crimes = sum(Total_Crimes), .groups = 'drop') %>%
  top_n(20, Total_Crimes)

# Filter the main data to include only these top crime types
filtered_crimes <- holiday_crimes %>%
  filter(Crm.Cd.Desc %in% top_crime_types$Crm.Cd.Desc)

# Plot with top crime types
fc <- ggplot(filtered_crimes, aes(x = reorder(Crm.Cd.Desc, -Total_Crimes), y = Total_Crimes, fill = as.factor(Year))) +
  geom_bar(stat = "identity", position = position_dodge()) +
  labs(title = "Top 20 Crime Types During Holidays", x = "Crime Type", y = "Total Crimes") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  coord_flip()  # This flips the axes to make the plot horizontal

ggplotly(fc)

# Annual trend of total crimes during November and December
annual_holiday_crime_trends <- holiday_crimes %>%
  group_by(Year) %>%
  summarise(Total_Crimes = sum(Total_Crimes), .groups = 'drop')

ahc <- ggplot(annual_holiday_crime_trends, aes(x = Year, y = Total_Crimes)) +
  geom_line(group=1, color="blue") +
  geom_point(color="red") +
  labs(title = "Annual Holiday Crime Trends", x = "Year", y = "Total Crimes")

ggplotly(ahc)

# Add a day of the week column
crime$Day_of_Week <- wday(crime$Date.Rptd, label = TRUE, abbr = FALSE)

# Summarize crimes by day of the week during the holiday months
weekday_holiday_crime_summary <- crime %>%
  filter(Month %in% c(11, 12)) %>%
  group_by(Day_of_Week) %>%
  summarise(Total_Crimes = n(), .groups = 'drop') %>%
  arrange(Day_of_Week)

dow <- ggplot(weekday_holiday_crime_summary, aes(x = Day_of_Week, y = Total_Crimes, fill = Day_of_Week)) +
  geom_bar(stat = "identity") +
  labs(title = "Crime Distribution by Day of the Week During Holidays", x = "Day of the Week", y = "Total Crimes")

ggplotly(dow)

Seasonal Trends: The first plot, displaying monthly crime trends by year, shows distinct seasonal fluctuations. Typically, crime rates appear to increase during certain months, which might correlate with seasonal activities or societal patterns. Identifying these peaks can help in planning better law enforcement deployment during high-risk times.

Top Crimes During Holidays: The list and plot of top crime types during November and December for each year reveal that certain types of crimes, such as vehicle theft, burglary, and assault, consistently rank high during the holiday seasons. This pattern suggests a need for targeted preventive measures during these periods when specific crimes spike, possibly due to the increased opportunity (with many homes potentially left empty and more retail activity).

Visual Accessibility: The bar chart displaying top 20 crime types during the holidays uses a horizontal layout, making it easier to read and compare the frequency of different crimes. This visualization helps stakeholders quickly identify the most prevalent crimes during the holiday seasons, which could inform public awareness campaigns and policing strategies.

Detailed Examination: The detailed breakdown by crime type, year, and month provides a granular view of crime trends, offering insights not only into the ‘what’ and ‘when’ but potentially the ‘why’ of crime patterns. For instance, understanding that thefts increase during November and December could be linked to the higher volume of shopping and unattended properties during the holiday season.

*** END OF QUESTION 3 ***

Question 4

How does the type or frequency of crimes in Los Angeles vary by victim descent, and are there significant geographic patterns (based on latitude and longitude) associated with specific victim groups?

library(tidyverse)
library(ggplot2)
library(sf)
library(cluster)
library(ggmap)

library(dplyr)
names(crime_data)

##  [1] "DR_NO"        "Date.Rptd"    "DATE.OCC"     "TIME.OCC"     "AREA"        
##  [6] "AREA.NAME"    "Crm.Cd"       "Crm.Cd.Desc"  "Vict.Age"     "Vict.Sex"    
## [11] "Vict.Descent" "Weapon.Desc"  "LOCATION"     "LAT"          "LON"

crime_frequency <- crime_data %>%
summarise(Frequency = n()) %>%
arrange(desc(Frequency))

1. Plotting data plots for Victim descent

crime_data %>%
  group_by(Vict.Descent) %>%
  summarise(Frequency = n()) %>%
  ggplot(aes(x = Vict.Descent, y = Frequency, fill = Vict.Descent)) +
  geom_bar(stat = "identity") +
  labs(title = "Total Crimes by Victim Descent", x = "Victim Descent", y = "Frequency") +
  theme_minimal()

crime_data$Time_Period <- cut(crime_data$TIME.OCC, 
                              breaks = c(0, 600, 1200, 1800, 2400), 
                              labels = c("Night", "Morning", "Afternoon", "Evening"))

crime_data %>%
  group_by(Vict.Descent, Time_Period) %>%
  summarise(Frequency = n()) %>%
  ggplot(aes(x = Vict.Descent, y = Time_Period, fill = Frequency)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "lightblue", high = "darkblue") +
  labs(title = "Crime Frequency by Victim Descent and Time of Day", x = "Victim Descent", y = "Time of Day") +
  theme_minimal()

library(lubridate)

crime_data$Date.OCC <- as.Date(crime_data$DATE.OCC, format = "%m/%d/%Y")
crime_data$Month <- floor_date(crime_data$Date.OCC, "month")

crime_data %>%
  group_by(Month, Vict.Descent) %>%
  summarise(Frequency = n()) %>%
  ggplot(aes(x = Month, y = Frequency, color = Vict.Descent)) +
  geom_line(size = 1) +
  labs(title = "Crime Trend Over Time by Victim Descent", x = "Year", y = "Frequency") +
  theme_minimal()

  theme_minimal()

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library(dplyr)
library(ggplot2)

crime_distribution <- crime_data %>%
  group_by(Vict.Descent) %>%
  summarise(Frequency = n(), .groups = "drop") %>%
  mutate(Percentage = Frequency / sum(Frequency) * 100)

ggplot(crime_distribution, aes(x = "", y = Percentage, fill = Vict.Descent)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y") +
  labs(title = "Distribution of Crimes by Victim Descent",
       fill = "Victim Descent") +
  theme_minimal() +
  theme(axis.text.x = element_blank())

library(ggplot2)
library(dplyr)

victim_counts <- crime_data %>%
  count(Vict.Descent)

ggplot(victim_counts, aes(x = Vict.Descent, y = n)) +
  geom_bar(stat = "identity") +
  labs(title = "Count of Crimes by Victim Descent",
       x = "Victim Descent",
       y = "Count of Crimes") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  theme_minimal()

# ANOVA for Longitude
anova_lon <- aov(LON ~ Vict.Descent, data = crime_data)
summary(anova_lon)

##                  Df   Sum Sq Mean Sq F value Pr(>F)    
## Vict.Descent     20    10754     538    16.8 <2e-16 ***
## Residuals    986479 31613033      32                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# ANOVA for Latitude
anova_lat <- aov(LAT ~ Vict.Descent, data = crime_data)
summary(anova_lat)

##                  Df  Sum Sq Mean Sq F value Pr(>F)    
## Vict.Descent     20    1380    69.0    25.9 <2e-16 ***
## Residuals    986479 2631056     2.7                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

library(dplyr)
library(tidyr)

unique_values <- unique(crime_data$Vict.Descent)
print(unique_values)

##  [1] "O" "X" "H" "B" "W" ""  "A" "K" "C" "J" "F" "I" "V" "S" "P" "Z" "G" "U" "D"
## [20] "L" "-"

relevant_data <- crime_data %>%
  select(LON, LAT, Vict.Descent)

relevant_data$Vict.Descent <- as.factor(relevant_data$Vict.Descent)

dummy_data <- model.matrix(~ Vict.Descent - 1, data = relevant_data)

combined_data <- cbind(relevant_data[, c("LON", "LAT")], dummy_data)

cor_matrix <- cor(combined_data, use = "pairwise.complete.obs")


head(cor_matrix)

##                     LON       LAT Vict.Descent Vict.Descent- Vict.DescentA
## LON            1.00e+00 -9.98e-01    -0.013629     -7.61e-05      -0.00289
## LAT           -9.98e-01  1.00e+00     0.012784      9.36e-05       0.00345
## Vict.Descent  -1.36e-02  1.28e-02     1.000000     -5.78e-04      -0.05999
## Vict.Descent- -7.61e-05  9.36e-05    -0.000578      1.00e+00      -0.00021
## Vict.DescentA -2.89e-03  3.45e-03    -0.059991     -2.10e-04       1.00000
## Vict.DescentB  3.68e-03 -1.21e-02    -0.161268     -5.66e-04      -0.05870
##               Vict.DescentB Vict.DescentC Vict.DescentD Vict.DescentF
## LON                0.003681     -2.62e-03     -3.69e-04     -1.55e-03
## LAT               -0.012099      2.43e-03      3.14e-04      2.86e-03
## Vict.Descent      -0.161268     -2.70e-02     -3.83e-03     -2.78e-02
## Vict.Descent-     -0.000566     -9.45e-05     -1.34e-05     -9.74e-05
## Vict.DescentA     -0.058699     -9.81e-03     -1.40e-03     -1.01e-02
## Vict.DescentB      1.000000     -2.64e-02     -3.75e-03     -2.72e-02
##               Vict.DescentG Vict.DescentH Vict.DescentI Vict.DescentJ
## LON                2.10e-03      0.008348     -4.04e-05     -1.32e-03
## LAT               -2.08e-03     -0.005730     -7.27e-05      8.24e-04
## Vict.Descent      -3.47e-03     -0.263940     -1.28e-02     -1.59e-02
## Vict.Descent-     -1.22e-05     -0.000926     -4.49e-05     -5.57e-05
## Vict.DescentA     -1.26e-03     -0.096071     -4.66e-03     -5.78e-03
## Vict.DescentB     -3.39e-03     -0.258255     -1.25e-02     -1.55e-02
##               Vict.DescentK Vict.DescentL Vict.DescentO Vict.DescentP
## LON               -0.000673     -3.31e-04     -0.001218     -7.69e-04
## LAT                0.000709      1.68e-04      0.005302      8.65e-04
## Vict.Descent      -0.031004     -3.49e-03     -0.118112     -6.83e-03
## Vict.Descent-     -0.000109     -1.22e-05     -0.000414     -2.39e-05
## Vict.DescentA     -0.011285     -1.27e-03     -0.042991     -2.49e-03
## Vict.DescentB     -0.030336     -3.42e-03     -0.115568     -6.68e-03
##               Vict.DescentS Vict.DescentU Vict.DescentV Vict.DescentW
## LON               -2.98e-04     -7.15e-04     -3.67e-04     -0.004784
## LAT                7.31e-05      6.18e-04      5.05e-04      0.008464
## Vict.Descent      -3.03e-03     -5.95e-03     -1.37e-02     -0.203293
## Vict.Descent-     -1.06e-05     -2.09e-05     -4.82e-05     -0.000713
## Vict.DescentA     -1.10e-03     -2.17e-03     -5.00e-03     -0.073996
## Vict.DescentB     -2.97e-03     -5.82e-03     -1.34e-02     -0.198914
##               Vict.DescentX Vict.DescentZ
## LON                0.009028     -1.33e-03
## LAT               -0.011396      1.55e-03
## Vict.Descent      -0.139342     -9.47e-03
## Vict.Descent-     -0.000489     -3.32e-05
## Vict.DescentA     -0.050719     -3.45e-03
## Vict.DescentB     -0.136341     -9.27e-03

2. Correlation Matrix analysis

library(ggplot2)
library(reshape2)
library(corrplot)

numeric_data <- crime_data[sapply(crime_data, is.numeric)]

corr <- cor(numeric_data, use = "complete.obs")  # use "complete.obs" to handle missing values

corrplot(corr, method = "color", 
         addCoef.col = "black",      # Add correlation coefficients
         tl.col = "black",           # Text color for labels
         tl.srt = 45,                # Rotation of text labels
         number.cex = 0.9,           # Size of the correlation coefficient text
         tl.cex = 0.9,               # Size of the text label for variables
         col = colorRampPalette(c("navy", "white", "firebrick3"))(200), # Refined color palette
         title = "Correlation Matrix", # Title of the plot
         mar = c(1,1,2,1),           # Adjusted margins for spacing
         cl.cex = 0.8,               # Size of the color legend text
         cl.pos = "r",               # Position of the color legend to the right
         diag = FALSE)               # Remove diagonal for better clarity

ggplot(crime_data, aes(x = LON)) +
  geom_density(aes(fill = Vict.Descent), alpha = 0.5) +
  facet_wrap(~ Vict.Descent) +
  labs(title = "Geographic Patterns of Crime (Longitude) by Victim Descent", 
       x = "Longitude", 
       y = "Density") +
  theme_minimal()

ggplot(crime_data, aes(x = LAT)) +
  geom_density(aes(fill = Vict.Descent), alpha = 0.5) +
  facet_wrap(~ Vict.Descent) +
  labs(title = "Geographic Patterns of Crime (Latitude) by Victim Descent", 
       x = "Latitude", 
       y = "Density") +
  theme_minimal()

anova_result_1 <- aov(LAT ~ AREA, data = crime_data)
summary(anova_result_1)

##                 Df  Sum Sq Mean Sq F value Pr(>F)    
## AREA             1    1590    1590     596 <2e-16 ***
## Residuals   986498 2630846       3                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova_result_2 <- aov(LON ~ AREA, data = crime_data)
summary(anova_result_2)

##                 Df   Sum Sq Mean Sq F value Pr(>F)    
## AREA             1     3155    3155    98.4 <2e-16 ***
## Residuals   986498 31620632      32                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# ANOVA for Longitude
anova_lon <- aov(LON ~ Vict.Descent, data = crime_data)
summary(anova_lon)

##                  Df   Sum Sq Mean Sq F value Pr(>F)    
## Vict.Descent     20    10754     538    16.8 <2e-16 ***
## Residuals    986479 31613033      32                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# ANOVA for Latitude
anova_lat <- aov(LAT ~ Vict.Descent, data = crime_data)
summary(anova_lat)

##                  Df  Sum Sq Mean Sq F value Pr(>F)    
## Vict.Descent     20    1380    69.0    25.9 <2e-16 ***
## Residuals    986479 2631056     2.7                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Filter data for two specific victim descent groups and non-missing lat/lon values
geo_subset <- crime_data %>%
  filter(Vict.Descent %in% c("H", "B"), !is.na(LAT), !is.na(LON))

# Ensure the data has exactly two levels for Vict.Descent
if (length(unique(geo_subset$Vict.Descent)) == 2) {
  # T-test for differences in latitude and longitude between the two groups
  latitude_t_test <- t.test(LAT ~ Vict.Descent, data = geo_subset)
  longitude_t_test <- t.test(LON ~ Vict.Descent, data = geo_subset)
  
  # Display results
  list(Latitude_Test = latitude_t_test, Longitude_Test = longitude_t_test)
} else {
  print("Data does not have exactly 2 groups for Vict.Descent. Please check the filtering.")
}

## $Latitude_Test
## 
##  Welch Two Sample t-test
## 
## data:  LAT by Vict.Descent
## t = -6, df = 3e+05, p-value = 6e-10
## alternative hypothesis: true difference in means between group B and group H is not equal to 0
## 95 percent confidence interval:
##  -0.0465 -0.0242
## sample estimates:
## mean in group B mean in group H 
##            33.9            34.0 
## 
## 
## $Longitude_Test
## 
##  Welch Two Sample t-test
## 
## data:  LON by Vict.Descent
## t = -1, df = 3e+05, p-value = 0.3
## alternative hypothesis: true difference in means between group B and group H is not equal to 0
## 95 percent confidence interval:
##  -0.0590  0.0185
## sample estimates:
## mean in group B mean in group H 
##            -118            -118

Question 5

Which crimes in Los Angeles exhibited the highest weapon usage, and which ethnicities and genders showed the most significant weapon involvement over the last five years?

1. Feature Selection and Data Cleaning for Weapons Used

la_crime <- crime %>%
  filter(!is.na(Weapon.Used.Cd))

tail(la_crime)

##            DR_NO  Date.Rptd               DATE.OCC TIME.OCC AREA  AREA.NAME
## 326363 241807402 2024-03-12 03/12/2024 12:00:00 AM      630   18  Southeast
## 326364 240405919 2024-02-27 02/26/2024 12:00:00 AM     1545    4 Hollenbeck
## 326365 241604405 2024-01-14 01/13/2024 12:00:00 AM      720   16   Foothill
## 326366 242004546 2024-01-16 01/16/2024 12:00:00 AM     1510   20    Olympic
## 326367 240104953 2024-01-15 01/15/2024 12:00:00 AM      100    1    Central
## 326368 240309674 2024-04-24 04/24/2024 12:00:00 AM     1500    3  Southwest
##        Rpt.Dist.No Part.1.2 Crm.Cd
## 326363        1841        2    624
## 326364         422        1    761
## 326365        1673        1    230
## 326366        2013        2    624
## 326367         101        2    745
## 326368         358        1    230
##                                           Crm.Cd.Desc
## 326363                       BATTERY - SIMPLE ASSAULT
## 326364                                BRANDISH WEAPON
## 326365 ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
## 326366                       BATTERY - SIMPLE ASSAULT
## 326367       VANDALISM - MISDEAMEANOR ($399 OR UNDER)
## 326368 ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT
##                              Mocodes Vict.Age Vict.Sex Vict.Descent Premis.Cd
## 326363                1300 1220 0400       33        M            H       101
## 326364           0216 0334 0371 0906       18        M            H       102
## 326365           0400 1822 1310 0416       36        M            H       101
## 326366 2047 0400 0429 0444 1202 1822       80        F            O       124
## 326367                0329 0400 0416        0        X            X       503
## 326368 1822 0334 0416 0445 0449 1202       70        F            W       102
##        Premis.Desc Weapon.Used.Cd
## 326363      STREET            400
## 326364    SIDEWALK            101
## 326365      STREET            207
## 326366    BUS STOP            400
## 326367       HOTEL            500
## 326368    SIDEWALK            308
##                                           Weapon.Desc Status Status.Desc
## 326363 STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)     AO Adult Other
## 326364                                       REVOLVER     IC Invest Cont
## 326365                                    OTHER KNIFE     IC Invest Cont
## 326366 STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)     IC Invest Cont
## 326367                    UNKNOWN WEAPON/OTHER WEAPON     IC Invest Cont
## 326368                                          STICK     IC Invest Cont
##        Crm.Cd.1 Crm.Cd.2 Crm.Cd.3 Crm.Cd.4
## 326363      624       NA       NA       NA
## 326364      761       NA       NA       NA
## 326365      230       NA       NA       NA
## 326366      624       NA       NA       NA
## 326367      745       NA       NA       NA
## 326368      230       NA       NA       NA
##                                       LOCATION                    Cross.Street
## 326363         106TH                        ST                        BROADWAY
## 326364         THOMAS                       ST BALDWIN                      ST
## 326365 8700    LANKERSHIM                   BL                                
## 326366         HOBART                       BL 3RD                          ST
## 326367 1300 W  SUNSET                       BL                                
## 326368         FLOWER                       ST JEFFERSON                    BL
##         LAT  LON Year Month Day Day_of_Week
## 326363 33.9 -118 2024     3  12     Tuesday
## 326364 34.1 -118 2024     2  27     Tuesday
## 326365 34.2 -118 2024     1  14      Sunday
## 326366 34.1 -118 2024     1  16     Tuesday
## 326367 34.1 -118 2024     1  15      Monday
## 326368 34.0 -118 2024     4  24   Wednesday

cols_to_remove <- c(
   "Mocodes", "Rpt.Dist.No", "Part.1.2", "Crm.Cd.2","Crm.Cd.3","Crm.Cd.4",
  "Premis_cd","Premis.Desc", "Status", "Status.Desc","Cross.Street"
)

# Drop the specified columns
la_crime<- la_crime[, !(names(la_crime) %in% cols_to_remove)]

la_crime <- la_crime %>% rename(
  Division_NO = 'DR_NO',
  Date_Reported = 'Date.Rptd',
  Date_Occurred = 'DATE.OCC',
  Time_Occurred = 'TIME.OCC',
  Area_Code = 'AREA',
  Area_Name = 'AREA.NAME',
  Crime_Code = 'Crm.Cd',
  Crime_Description = 'Crm.Cd.Desc',
  Weapons_Used='Weapon.Used.Cd',    
  Weapons_Description='Weapon.Desc',
  Victim_Age = 'Vict.Age',
  Victim_Sex = 'Vict.Sex',
  Victim_Descent = 'Vict.Descent',
  Crime_Code_1 = 'Crm.Cd',
  Location = 'LOCATION',
  Latitude = 'LAT',
  Longitude = 'LON'
)

print("Data after removing unnecessary columns:")

## [1] "Data after removing unnecessary columns:"

print(names(la_crime))

##  [1] "Division_NO"         "Date_Reported"       "Date_Occurred"      
##  [4] "Time_Occurred"       "Area_Code"           "Area_Name"          
##  [7] "Crime_Code_1"        "Crime_Description"   "Victim_Age"         
## [10] "Victim_Sex"          "Victim_Descent"      "Premis.Cd"          
## [13] "Weapons_Used"        "Weapons_Description" "Crm.Cd.1"           
## [16] "Location"            "Latitude"            "Longitude"          
## [19] "Year"                "Month"               "Day"                
## [22] "Day_of_Week"

# Count occurrences of "H" in the Victim_Sex column
count_H <- sum(la_crime$Victim_Sex == "H", na.rm = TRUE)

# Print the result
print(count_H)

## [1] 27

# Remove rows where Victim_Sex is "H"
la_crime <- la_crime %>%
  filter(Victim_Sex != "H")

# Print the result to verify
nrow(la_crime)

## [1] 326341

2. Summarization and Plots of Top 10 crimes with Weapon Usage

# Summarize and find top 10 crimes with weapon usage
top_crimes <- la_crime %>%
  group_by(Crime_Description, Weapons_Description) %>%
  summarise(count = n(), .groups = 'drop') %>%
  arrange(desc(count)) %>%
  slice_head(n = 10) 

print(top_crimes)

## # A tibble: 10 × 3
##    Crime_Description                              Weapons_Description      count
##    <chr>                                          <chr>                    <int>
##  1 BATTERY - SIMPLE ASSAULT                       STRONG-ARM (HANDS, FIST… 67439
##  2 INTIMATE PARTNER - SIMPLE ASSAULT              STRONG-ARM (HANDS, FIST… 43968
##  3 CRIMINAL THREATS - NO WEAPON DISPLAYED         VERBAL THREAT            17559
##  4 ROBBERY                                        STRONG-ARM (HANDS, FIST… 14010
##  5 ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT HAND GUN                  7658
##  6 INTIMATE PARTNER - AGGRAVATED ASSAULT          STRONG-ARM (HANDS, FIST…  7511
##  7 ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT STRONG-ARM (HANDS, FIST…  7122
##  8 ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT UNKNOWN WEAPON/OTHER WE…  5888
##  9 ROBBERY                                        HAND GUN                  5830
## 10 BATTERY - SIMPLE ASSAULT                       UNKNOWN WEAPON/OTHER WE…  5725

# Calculate percentages
top_crimes <- top_crimes %>%
  mutate(percentage = count / sum(count) * 100)

summary <- top_crimes %>%
  group_by(Crime_Description) %>%
  summarise(total_count = sum(count)) %>%
  mutate(percentage = (total_count / sum(total_count)) * 100)

custom_colors <- c(
  "BATTERY - SIMPLE ASSAULT" = "#FF6F61",
  "INTIMATE PARTNER - SIMPLE ASSAULT" = "#6B5B95",
  "CRIMINAL THREATS - NO WEAPON DISPLAYED" = "#88B04B",
  "ROBBERY" = "#F7CAC9",
  "ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT" = "#92A8D1",
  "INTIMATE PARTNER - AGGRAVATED ASSAULT" = "#65bdc8",
  "UNKNOWN WEAPON/OTHER WEAPON" = "#B565A7"
)

# Create the pie chart
ggplot(summary, aes(x = factor(1), y = total_count, fill = Crime_Description)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar(theta="y", start = 0) +
  labs(title = "Distribution of Top 10 Crimes in LA with Most Weapons Usage") +
  theme_void() +
  scale_fill_manual(values = custom_colors) +
  theme(legend.position = "right") +
  geom_text(aes(label = sprintf("%.1f%%", percentage)), 
            position = position_stack(vjust = 0.5), color = "black")

3. Total Weapons used against various victim’s sex and victim’s descent.

weapons_sum <- la_crime %>%
  group_by(Victim_Sex, Victim_Descent, Weapons_Description,Crime_Description) %>%
  summarize(total_count = n(), .groups = 'drop') %>%
  top_n(20, total_count) %>%
  arrange(desc(total_count)) 

head(weapons_sum)

## # A tibble: 6 × 5
##   Victim_Sex Victim_Descent Weapons_Description    Crime_Description total_count
##   <chr>      <chr>          <chr>                  <chr>                   <int>
## 1 F          H              STRONG-ARM (HANDS, FI… INTIMATE PARTNER…       18269
## 2 M          H              STRONG-ARM (HANDS, FI… BATTERY - SIMPLE…       18018
## 3 F          H              STRONG-ARM (HANDS, FI… BATTERY - SIMPLE…       16075
## 4 F          B              STRONG-ARM (HANDS, FI… INTIMATE PARTNER…        8371
## 5 F          B              STRONG-ARM (HANDS, FI… BATTERY - SIMPLE…        7486
## 6 M          W              STRONG-ARM (HANDS, FI… BATTERY - SIMPLE…        7125

 custom_labels <- c("Hand Gun", "Strong Arm", "Verbal Threat")
  ggplot(weapons_sum, aes(x = Weapons_Description, y = total_count, fill = Victim_Sex)) +
    geom_bar(stat = "identity") +
    facet_wrap(~ Victim_Descent) +  # Separate panels for each descent
    labs(title = "Total Weapons Used by Victim Sex and Descent",
         x = "Weapon Description",
         y = "Total Count") +
    scale_x_discrete(labels = custom_labels) +
    scale_y_continuous(limits = c(0, max(weapons_sum$total_count) * 1.1)) +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

Analysis 1) Hand Gun usage is notably higher for Black (B) and Hispanic (H) victims, with males being disproportionately represented. Strong Arm (physical force) incidents are more frequent for White (W) victims, with a balanced representation of both male and female victims.

Across all categories, males (teal) are more frequently victims, especially in cases involving firearms. Female victims (red) appear more frequently in non-weapon-based incidents such as Strong Arm cases (e.g., assaults).
Hispanic (H) victims have a high rate of gun-related incidents involving male victims. White (W) victims show a high count for Strong Arm incidents, with females almost matching male victims. Other (O) group mainly experiences incidents involving Strong Arm, with no significant firearm usage.
This visualization suggests that firearms are predominantly involved in incidents affecting Black and Hispanic males. Meanwhile, physical force is more common among White victims. Gender-based victimization patterns are also evident, with men more frequently involved in firearm-related incidents, whereas women are more often victims in physical confrontations.

4. Total Crimes against Victim’s Sex and Descent

# Total crimes by sex and ethnicity
total_crimes_by_sex_ethnicity <- la_crime %>%
  filter(!is.na(Victim_Sex) & !is.na(Victim_Descent)) %>% 
  group_by(Victim_Sex, Victim_Descent) %>%
  summarise(total_count = n(), .groups = 'drop') %>%
  arrange(desc(total_count))

# Print the result
head(total_crimes_by_sex_ethnicity)

## # A tibble: 6 × 3
##   Victim_Sex Victim_Descent total_count
##   <chr>      <chr>                <int>
## 1 M          H                    77006
## 2 F          H                    73824
## 3 F          B                    38681
## 4 M          B                    31035
## 5 M          W                    29463
## 6 F          W                    24115

descent_labels <- c(
A = "Other Asian",B = "Black", C = "Chinese", D = "Cambodian", F = "Filipino", G = "Guamanian",
H = "Hispanic/Latin/Mexican", I = "American Indian/Alaskan Native", J = "Japanese", K = "Korean",
L = "Laotian", O = "Other", P = "Pacific Islander", S = "Samoan", U = "Hawaiian", V = "Vietnamese",
W = "White", X = "Unknown", Z = "Asian Indian"
)


# Create a heatmap
ggplot(total_crimes_by_sex_ethnicity, aes(x = factor(Victim_Descent, levels = names(descent_labels)), 
                                            y = Victim_Sex, fill = total_count)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "lightblue", high = "darkblue") +
  labs(title = "Heatmap of Total Crimes by Victim Sex and Ethnicity",
       x = "Ethnicity",
       y = "Victim Sex",
       fill = "Total Crimes") +
  scale_x_discrete(labels = descent_labels) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Analysis:

Males: 1) For several ethnic groups, males are involved in the majority of crimes compared to females. Black and Hispanic/Latino/Mexican groups exhibit the highest crime counts, indicated by darker shades.

Females: 1) Crimes against females appear less frequent overall, with relatively lighter shades. However, there is still notable representation among Black and Hispanic/Latino/Mexican victims, though to a lesser extent than males.

Unknown/Other Sex (X): 1) This category has significant counts across several ethnicities, particularly Black, Hispanic/Latino/Mexican, and White groups.

White victims also show a high frequency but with a more balanced sex distribution compared to other ethnic groups. Asian and Pacific Islander groups generally exhibit lower crime counts, as indicated by the lighter shades.

The gender distribution also shows that males are disproportionately affected across most ethnic groups, especially in the most impacted categories.

5. Test of Independence

#Test for Independence
contingency_Sex <- table(la_crime$Victim_Sex, la_crime$Crime_Description)

contingency_descent <- table(la_crime$Victim_Descent, la_crime$Crime_Description)

chi_squared_result <- chisq.test(contingency_Sex)
print(chi_squared_result)

## 
##  Pearson's Chi-squared test
## 
## data:  contingency_Sex
## X-squared = 1e+05, df = 363, p-value <2e-16

chi_squared_result1 <- chisq.test(contingency_descent)
print(chi_squared_result1)

## 
##  Pearson's Chi-squared test
## 
## data:  contingency_descent
## X-squared = 95725, df = 2299, p-value <2e-16

There is a statistically significant association between the two categorical variables for both tests conducted between Victim Sex, Descent and Crime. Hence we can accept the alternate hypothesis that they are dependent on each other.

*** END OF QUESTION 5 ***

L.A. Crime Scene Investigation: Data-Driven Detectives

Abhinaysai Kamineni Lasya Raghavendra Neeraj Magadum Aakash Hariharan Amogh Ramagiri

2024-10-26